Faster heart rate and muscular oxygen uptake kinetics in type 2 diabetes patients following endurance training
Bibliographic record
Abstract
Cardiorespiratory kinetics were analyzed in type 2 diabetes patients before and after a 12-week endurance exercise-training intervention. It was hypothesized that muscular oxygen uptake and heart rate (HR) kinetics would be faster after the training intervention and that this would be detectable using a standardized work rate protocol with pseudo-random binary sequences. The cardiorespiratory kinetics of 13 male sedentary, middle-aged, overweight type 2 diabetes patients (age, 60 ± 8 years; body mass index, 33 ± 4 kg·m−2) were tested before and after the 12-week exercise intervention. Subjects performed endurance training 3 times a week on nonconsecutive days. Pseudo-random binary sequences exercise protocols in combination with time series analysis were used to estimate kinetics. Greater maxima in cross-correlation functions (CCFmax) represent faster kinetics of the respective parameter. CCFmax of muscular oxygen uptake (pre-training: 0.31 ± 0.03; post-training: 0.37 ± 0.1, P = 0.024) and CCFmax of HR (pre-training: 0.25 ± 0.04; post-training: 0.29 ± 0.06, P = 0.007) as well as peak oxygen uptake (pre-training: 24.4 ± 4.7 mL·kg−1·min−1; post-training: 29.3 ± 6.5 mL·kg−1·min−1, P = 0.004) increased significantly over the course of the exercise intervention. In conclusion, kinetic responses to changing work rates in the moderate-intensity range are similar to metabolic demands occurring in everyday habitual activities. Moderate endurance training accelerated the kinetic responses of HR and muscular oxygen uptake. Furthermore, the applicability of the used method to detect these accelerations was demonstrated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".